1
Algorithmic and Minimax Complexities in Kernel Bandits
探究核赌博机问题的算法与极小极大复杂度，理论机器学习新进展
arXiv:2606.11171v1 Announce Type: new Abstract: Gaussian-process upper confidence bound (GP-UCB) and decision-estimation-coefficient (DEC) methods may…
2
Sinkhorn Normalization of Diffusion Kernels
一篇介绍扩散核Sinkhorn归一化新方法的学术论文，可帮助机器学习研究者更高效地处理核矩阵与概率流。
arXiv:2507.06161v2 Announce Type: replace Abstract: Smoothing a signal based on local neighborhoods is a core operation in machine learning and geomet…
3
Trained quantum neural networks are Gaussian processes
训练后的量子神经网络展现出高斯过程行为，揭示其与经典核方法的深层联系，为量子机器学习理论提供新视角
arXiv:2402.08726v2 Announce Type: replace-cross Abstract: We study quantum neural networks made by parametric one-qubit gates and fixed two-qubit gate…
4
Manifold Random Features
探索流形上的随机特征方法，为核方法在复杂数据空间中的应用提供新思路。
arXiv:2602.03797v3 Announce Type: replace Abstract: We present a new paradigm for creating random features to approximate bi-variate functions (in par…